Deepak Ingole

About

Dr. Deepak Ingole is a Senior Scientist at the Zurich University for Applied Sciences (ZHAW). His research interests involve optimization, model predictive control, learning-based modeling and control, embedded systems, and real-time control. He has been engaged in designing and developing advanced control algorithms to tackle complex problems in energy, automotive, electric vehicle, transportation, mechatronics, process control, and biomedical fields.

In his past roles as SME - Control Systems at KPIT India, he was responsible for overseeing a team of engineers who developed and tested software-defined vehicle (SDV) software for passenger and commercial vehicles. He also served as a Senior Consultant - Control Systems at Enoda Tech, UK.

In his current role, he is developing advanced data-driven control and optimization algorithms for Industry 4.0 solutions, which include robotics, 3D printing, digital twins, etc.

Employment

• ZHAW Zurich University of Applied Sciences Senior Research Scientist
  2025 - Present
• KPIT India Subject Matter Expert - Control Systems
  2023 - 2024
• Enoda Tech Senior Consultatnt - Control Systems
  2022 - 2023
• KU Leuven Postdoc
  2020 - 2021
• IFSTTAR Site de Lyon-Bron Post-doc researcher
  2017 - 2020
• Slovak University of Technology in Bratislava Researcher
  2014 - 2017
• Government College of Engineering Project Engineer
  2012 - 2014

Education

• Slovak University of Technology in Bratislava PhD
  2014 - 2017
• Government College of Engineering M.Tech
  2010 - 2012
• University of Pune B.E.
  2006 - 2010

Projects & Funding

Projects & funding information is unavailable.

Publications (24)

• Ultra-Fast Nonlinear Model Predictive Control for Motion Control of Autonomous Light Motor Vehicles
  World Electric Vehicle Journal 2024 DOI: 10.3390/wevj15070299
  +Save
• A Memory Efficient FPGA Implementation of Offset-Free Explicit Model Predictive Controller
  IEEE Transactions on Control Systems Technology 2022 DOI: 10.1109/TCST.2022.3168493
  +Save
• The Role of Trip Lengths Calibration in Model-Based Perimeter Control Strategies
  IEEE Transactions on Intelligent Transportation Systems 2022 DOI: 10.1109/TITS.2021.3049679
  +Save
• Embedded Implementation of Explicit Model Predictive Control
  2017
  +Save
• Model predictive control of a combined electrolyzer-fuel cell educational pilot plant
  Process Control (PC), 2017 21st International Conference on 2017
  +Save
• Offset-free hybrid model predictive control of bispectral index in anesthesia
  Process Control (PC), 2017 21st International Conference on 2017
  +Save
• Performance evaluation of PID, LQR and MPC for DC motor speed control
  Convergence in Technology (I2CT), 2017 2nd International Conference for 2017
  +Save
• Reducing Memory Footprints in Explicit Model Predictive Control using Universal Numbers
  IFAC-PapersOnLine 2017
  +Save
• Explicit Model Predictive Control of a Fuel Cell
  The European Conference on Computational Optimization 2016
  +Save
• Teaching classical and advanced control of binary distillation column
  IFAC-PapersOnLine 2016
  +Save